Changes in grassland community composition at human-induced edges in the south Okanagan
Bibliographic record
Abstract
The grasslands of the Okanagan Valley, in southern British Columbia, Canada, are under intense development pressure. Alteration of biotic and abiotic conditions at the edges of remnant habitat patches is one of the key consequences of habitat fragmentation. Such edge effects likely diminish nonlinearly with increased distance from the edge, and significant changes are expected to be greater than the natural spatial variation within the interior of a habitat patch. Furthermore, habitat adjacent to more intensively managed areas, like paved roads and fruit crops, should be more affected at the edge than habitat fragmented by less intensively managed areas, like dirt roads. I used nonlinear canonical analysis of principal coordinates (NCAP), which characterizes nonlinear gradients in species composition, to test if edge effects were present in grassland communities next to roads and cropland. Variation partitioning was also used to determine the relative importance of key environmental factors in predicting compositional change at edges. Nonlinear shifts in community composition were more frequent at the edges of paved roads and fruit crops than at control sites in the interior of grassland patches. On average, 90% of the compositional change occurred within 28 m of the edge. Variation partitioning suggested that nonlinear responses at developed edges were due to true edge effects and not natural gradients, since a significant proportion of the nonlinear change in community composition was related to distance from the edge independently of the soil environmental variables at all types of human-developed edges, but not at the control site. The soil factors that best predicted compositional changes were soil pH and Cu/Mn at paved roads, soil pH and mineralizable N at the edges of fruit crops, and soil resistance at the edges of dirt roads, while soil texture and mobile cations best explained community variation at one control site with a significant nonlinear gradient. Comparisons between edge and interior plots revealed decreased cryptogam cover and an increase in the proportion of exotic species at the edges, but changes were significant only at paved roads. In sum, biotic and abiotic edge effects were present in the selected grasslands, particularly at roadsides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".